Alerting the public when heat may harm their health is a crucial service, especially considering that extreme heat events will be more frequent under climate change. Current practice for issuing heat alerts in the US does not take advantage of modern data science methods for optimizing local alert criteria. Specifically, application of reinforcement learning (RL) has the potential to inform more health-protective policies, accounting for regional and sociodemographic heterogeneity as well as sequential dependence of alerts. In this work, we formulate the issuance of heat alerts as a sequential decision making problem and develop modifications to the RL workflow to address challenges commonly encountered in environmental health settings. Key modifications include creating a simulator that pairs hierarchical Bayesian modeling of low-signal health effects with sampling of real weather trajectories (exogenous features), constraining the total number of alerts issued as well as preventing alerts on less-hot days, and optimizing location-specific policies. Post-hoc contrastive analysis offers insights into scenarios when using RL for heat alert issuance may protect public health better than the current or alternative policies. This work contributes to a broader movement of advancing data-driven policy optimization for public health and climate change adaptation.

翻译：在气候变化导致极端高温事件愈发频繁的背景下，及时向公众发布可能危害健康的高温预警是一项至关重要的公共服务。当前美国热浪警报发布实践尚未充分利用现代数据科学方法来优化本地预警标准。具体而言，应用强化学习（RL）具备制定更有利于健康防护政策的潜力，可兼顾区域和社会人口异质性以及预警发布的时序依赖性。本研究将热浪警报发布问题构建为序贯决策问题，并针对环境健康领域的常见挑战对RL工作流进行了改进。核心改进包括：构建将低信号健康效应的分层贝叶斯建模与实际天气轨迹（外生特征）采样相结合的系统仿真器，约束总预警发布数量并避免在非高温日发布预警，以及优化区域特异性政策。事后对比分析揭示了在特定场景下，采用RL方法发布热浪警报可能比当前政策或替代政策更有效地保护公众健康。本研究为推进数据驱动的公共卫生政策优化与气候变化适应能力建设提供了重要支撑。